Object-oriented stripe structured-light vision-guided robot
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Abstract:
The stripe laser based stereo vision is often used in robot vision-guided system in the eye-in-hand configuration. The 3D scene is reconstructed from many 3D stripes obtained in stripe laser based stereo vision. But 3D objects can not be recognized by 3D stripe information. In 3D cluttered scene, the recognition of 3D objects is also difficult due to the object pose and match. In fact, the video from camera of stripe laser based stereo vision can be benefit to recognize 3D objects. This paper proposes an approach of the object-oriented vision-guided robot that video segmentation, tracking and recognition are used to guide robot to reduce the complexity of 3D object detection, recognition and pose estimation. Experimental results demonstrate the effectiveness of the approach.Keywords:
Structured Light
Stereo cameras
Machine Vision
Computer stereo vision
Binocular stereo vision is an important branch of the research area in computer vision. Stereo matching is the most important process in binocular vision. In this paper, a new stereo matching scheme using shape-based matching (SBM) is presented to improve the depth reconstruction method of binocular stereo vision systems. The method works in two steps. First, an operator registers the pattern including the key features of an object to be measured. Then during the operation stage, the stereo camera snaps stereo images and finds the patterns in right and left images separately by means of the SBM. The 3D positions of the object are calculated by using the corresponding points of the stereo images and the projection matrices of the stereo camera. Since we apply robust image processing algorithms, such as the SBM, the proposed method becomes more reliable than the conventional stereo vision systems.
Computer stereo vision
Stereo cameras
Binocular disparity
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<p>In modern research, the most studied step of stereo vision algorithms is the process of pixel correspondence, better known as stereo matching. As of now, researchers are attempting to integrate optimization techniques with stereo matching to improve stereo system performance. This paper seeks to provide an in-depth explanation of the stereo vision process in general and find value in the application of optimization techniques to stereo-matching algorithms. To analyze and implement a sound stereo vision algorithm as well as an optimized matching algorithm, scholarly sources involving stereo vision and optimization techniques were studied. After implementing a standard stereo-matching algorithm and an algorithm that involves a famous optimization technique known as Dynamic Programming, I found that there was a significant increase in both accuracy and efficiency in the depth estimates provided by the algorithm.</p>
Computer stereo vision
Stereo cameras
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Stereo vision is a growing research domain which seeks the attention of various researchers to attain deeper scene extraction. This work provides an extensive analysis towards the stereo-matching algorithms and stereo-vision to resolve the problems related to it. The analysis towards the stereo matching technologies is executed with benchmark standards with the focus on stereo vision methods. Thus the comparison of stereo matching algorithms can be done through the implementation of stereo vision application in a particular domain so that the results for the algorithms are comparatively analyzed. In most cases, the analysis and comparison are performed with statistical analysis and emphasize the benefits of various stereo algorithms. Some approaches give higher computational cost with expected outcomes over the lower time frame and provides competency towards parallel processing. The results obtained from the various stereo matching algorithms through the identified different parameters of bad pixels.
Computer stereo vision
Stereo cameras
Benchmark (surveying)
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Computer stereo vision
Stereo cameras
Feature (linguistics)
Machine Vision
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Stereo cameras
Computer stereo vision
Stereo imaging
Machine Vision
Smart camera
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Stereo vision is a strong-crossed subject, and it has been one of the most hot topics in the study of computer.In more than 40 years of development, it has formed its own method and theory.This paper systematically reviews the present situation of stereo vision research, briefly introduces stereo vision system and 3D TV program production, and analyzes the error controlling through the production.
Stereo cameras
Computer stereo vision
Machine Vision
Stereo image
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In modern research, the most studied step of stereo vision algorithms is the process of pixel correspondence, better known as stereo matching. As of now, researchers are attempting to integrate optimization techniques with stereo matching to improve stereo system performance. This paper seeks to provide an in-depth explanation of the stereo vision process in general and find value in the application of optimization techniques to stereo-matching algorithms. To analyze and implement a sound stereo vision algorithm as well as an optimized matching algorithm, scholarly sources involving stereo vision and optimization techniques were studied. After implementing a standard stereo-matching algorithm and an algorithm that involves a famous optimization technique known as Dynamic Programming, I found that there was a significant increase in both accuracy and efficiency in the depth estimates provided by the algorithm.
Computer stereo vision
Stereo cameras
Optimization algorithm
Machine Vision
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<p>In modern research, the most studied step of stereo vision algorithms is the process of pixel correspondence, better known as stereo matching. As of now, researchers are attempting to integrate optimization techniques with stereo matching to improve stereo system performance. This paper seeks to provide an in-depth explanation of the stereo vision process in general and find value in the application of optimization techniques to stereo-matching algorithms. To analyze and implement a sound stereo vision algorithm as well as an optimized matching algorithm, scholarly sources involving stereo vision and optimization techniques were studied. After implementing a standard stereo-matching algorithm and an algorithm that involves a famous optimization technique known as Dynamic Programming, I found that there was a significant increase in both accuracy and efficiency in the depth estimates provided by the algorithm.</p>
Computer stereo vision
Stereo cameras
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Stereo vision has become an attractive topic research in the last decades. Many implementations such as the autonomous car, 3D movie, 3D object generation, are produced using this technique. The advantages of using two cameras in stereo vision are the disparity map between images. Disparity map will produce distance estimation of the object. Distance measurement is a crucial parameter for an autonomous car. The distance between corresponding points between the left and right images must be precisely measured to get an accurate distance. One of the most challenging in stereo vision is to find corresponding points between left and right images (stereo matching). This paper proposed distance measurement using stereo vision using Semi-Global Block Matching algorithm for stereo matching purpose. The object is captured using a calibrated stereo camera. The images pair then optimized using WLS Filter to reduce noises. The implementation results of this algorithm are furthermore converted to a metric unit for distance measurement. The result shows that the stereo vision distance measurement using Semi-Global Block Matching gives a good result. The obtained best result of this work contains error of less than 1% for 1m distance
Computer stereo vision
Stereo cameras
Distance measurement
Epipolar geometry
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Citations (17)
In this paper, a practical method of stereo vision, “subtraction stereo” is proposed. A huge number of studies have been carried out for stereo vision until now, and several practical stereo vision systems have been reported. However, what is called the correspondence problem that stereo matching becomes difficult and not robust for weak textures or recurrent patterns is inevitable for stereo vision. Subtraction stereo realizes robust measurement of range images by detecting moving regions with each camera first and then applying stereo matching for the detected moving regions. Detection of moving regions is carried out with a subtraction process. Concept and fundamental algorithm of subtraction stereo are introduced. Then measurement of three-dimensional position, height and width of a target object using the subtraction stereo is discussed. The basic algorithm is implemented on a commercially available stereo camera and the effectiveness of the subtraction stereo is verified by several experiments using the stereo camera. Although objects are restricted to moving ones, subtraction stereo gives sufficient information robustly for many applications such as surveillance.
Computer stereo vision
Subtraction
Stereo cameras
Stereo imaging
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